SoroushVahidi/scidocs huggingface dataset:
Low-profile dual-band circularly polarized microstrip antenna for GNSS applications
(2015)

This paper presents a design of a micro-strip circularly polarized antenna intended for the Global Navigation Satellite Systems (GNSS). The presented device is composed of a micro-strip slotted patch antenna printed on a Rogers RO3006 substrate, a foam layer of 2 mm thick and a wideband commercial 3-dB SMT coupler. The combined fullwave antenna results with the measured S-Parameters of the coupler shows very good performances in terms of antenna matching and axial ratio on larger bandwidths.
The PLEX Cards and its techniques as sources of inspiration when designing for playfulness
(2013)

Playfulness can be observed in all areas of human activity. It is an attitude of making activities more enjoyable. Designing for playfulness involves creating objects that elicit a playful approach and provide enjoyable experiences. In this paper, we introduce the design and evaluation of the PLEX Cards and its two related idea generation techniques. The cards were created to communicate the 22 categories of a playful experiences framework to designers and other stakeholders who wish to design for playfulness. We have evaluated the helpfulness of both the cards and their associated techniques in two studies. The results show that the PLEX Cards and its associated techniques are valuable sources of inspiration when designing for playfulness.
Cloud Federation: Effects of Federated Compute Resources on Quality of Service and Cost*
(2013)

Cloud Federation is one concept to confront challenges that still persist in Cloud Computing, such as vendor lock-in or compliance requirements. The lack of a standardized meaning for the term Cloud Federation has led to multiple conflicting definitions and an unclear prospect of its possible benefits. Taking a client-side perspective on federated compute services, we analyse how choosing a certain federation strategy affects Quality of Service and cost of the resulting service or application. Based on a use case, we experimentally prove our analysis to be correct and describe the different trade-offs that exist within each of the strategies.
The nucleotide sequence of a HMW glutenin subunit gene located on chromosome 1A of wheat (Triticum aestivum L.).
(1985)

A cloned 8.2 kb EcoRI fragment has been isolated from a genomic library of DNA derived from Triticum aestivum L. cv. Cheyenne. This fragment contains sequences related to the high molecular weight (HMW) subunits of glutenin, proteins considered to be important in determining the elastic properties of gluten. The cloned HMW subunit gene appears to be derived from chromosome 1A. The nucleotide sequence of this gene has provided new information on the structure and evolution of the HMW subunits. However, hybrid-selection translation experiments suggest that this gene is silent.
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
(2018)

Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need of capturing dynamics for prediction on a toy data set created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real world data sets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.
Operational capabilities development in mediated offshore software services models
(2008)

The paper expands theoretical and empirical understanding of capabilities development in the mediated offshore outsourcing model whereby a small or a medium-sized firm delivers offshore software services to a larger information technology firm that in turn contracts and interfaces with the actual end-client onshore firms. Such a mediated model has received little prior research attention, although it is common particularly among Chinese firms exporting services to Japan, the largest export market for Chinese software services. We conducted case studies in four China-based software companies to understand the mechanisms used to develop their operational capabilities. We focused on client-specific, process, and human resources capabilities that have been previously associated with vendor success. We found a range of learning mechanisms to build the capabilities in offshore firms. Results show that the development of human resources capabilities was most challenging in the mediated model; yet foundational for the development of the other capabilities. This paper contributes to the information systems literature by improving our understanding of the development of operational capabilities in smalland medium-sized Chinese firms that deploy the mediated model of offshore
Unsupervised detection of surface defects: A two-step approach
(2012)

In this paper, we focus on the problem of finding anomalies in surface images. Despite enormous research efforts and advances, it still remains a big challenge to be solved. This paper proposes a unified approach for defect detection. Our proposed method consists of two phases: (1) global estimation and (2) local refinement. First, we roughly estimate defects by applying a spectral-based approach in a global manner. We then locally refine the estimated region based on the distributions of pixel intensities derived from defect and defect-free regions. Experimental results show that the proposed method outperforms the previous defect detection methods and gives robust results even in noisy surface defect images.
The Ideology of Interactivity (or Video Games and Taylorization of Leisure)
(2003)

Interactivity is one of the key conceptual apparatuses through which video games have been theorized thus far. As many writers have noted, video games are distinct from other forms of media because player actions seem to have direct, immediate consequences in the world depicted onscreen. But in many ways, this “interactive” feature of video games tends to manifest itself as a relentless series of demands, or a way of disciplining player behavior. In this sense, it seems more accurate to describe the human-machine interface made possible by gaming as an aggressive form of “interpellation” or hailing. Drawing primarily upon the work of Louis Althusser, I argue that traditional theories of interactivity fail to acknowledge the work of video games—in other words, the extent to which video games define and reconstitute players as subjects of ideology.
Generalized Methods for the Design of Quasi-Ideal Symmetric and Asymmetric Coupled-Line Sections and Directional Couplers
(2011)

Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.
Bullying, cyberbullying, and mental health in young people.
(2014)

OBJECTIVE
To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls.


METHODS
Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%).


RESULTS
The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls.


CONCLUSIONS
Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.
Pivoted Document Length Normalization
(1996)

Automatic information retrieval systems have to deal with documents of varying lengths in a text collection. Document length normalization is used to fairly retrieve documents of all lengths. In this study, we ohserve that a normalization scheme that retrieves documents of all lengths with similar chances as their likelihood of relevance will outperform another scheme which retrieves documents with chances very different from their likelihood of relevance. We show that the retrievaf probabilities for a particular normalization method deviate systematically from the relevance probabilities across different collections. We present pivoted normalization, a technique that can be used to modify any normalization function thereby reducing the gap between the relevance and the retrieval probabilities. Training pivoted normalization on one collection, we can successfully use it on other (new) text collections, yielding a robust, collectzorz independent normalization technique. We use the idea of pivoting with the well known cosine normalization function. We point out some shortcomings of the cosine function andpresent two new normalization functions--pivoted unique normalization and piuotert byte size normalization.
Nearest neighbor pattern classification
(1967)

The case of n unity-variance random variables x1, XZ,. * *, x, governed by the joint probability density w(xl, xz, * * * x,) is considered, where the density depends on the (normalized) cross-covariances pii = E[(xi jzi)(xi li)]. It is shown that the condition holds for an “arbitrary” function f(xl, x2, * * * , x,) of n variables if and only if the underlying density w(xl, XZ, * * * , x,) is the usual n-dimensional Gaussian density for correlated random variables. This result establishes a generalized form of Price’s theorem in which: 1) the relevant condition (*) subsumes Price’s original condition; 2) the proof is accomplished without appeal to Laplace integral expansions; and 3) conditions referring to derivatives with respect to diagonal terms pii are avoided, so that the unity variance assumption can be retained. Manuscript received February 10, 1966; revised May 2, 1966. The author is with the Ordnance Research Laboratory, Pennsylvania State University, State College, Pa. RICE’S THEOREM and its various extensions ([l]-[4]) have had great utility in the determination of output correlations between zero-memory nonlinearities subjected to jointly Gaussian inputs. In its original form, the theorem considered n jointly normal random variables, x1, x2, . . . x,, with respective means 21, LE2, . . . ) Z, and nth-order joint probability density, P(z 1, .x2, . 1 . , :r,,) = (27p ply,, y . exp { -a F F ;;;, ~ (2,. 2,)(:r, 5,) I , (1) where IM,l is the determinant’ of J1,, = [p,,], Pr-. = E[(sr 5$.)(x, ,2J] = xvx, &IL:, is the correlation coefficient of x, and x,, and Ail,, is the cofactor of p.? in ilf,. From [l], the theorem statement is as follows: “Let there be n zero-memory nonlinear devices specified by the input-output relationship f<(x), i = 1, 2, . 1 . , n. Let each xi be the single input to a corresponding fi(x) Authorized licensed use limited to: West Virginia University. Downloaded on February 26, 2009 at 13:41 from IEEE Xplore. Restrictions apply.
A flexible coupling approach to multi-agent planning under incomplete information
(2012)

Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.
Image Processing Operations Identification via Convolutional Neural Network
(2017)

In recent years, image forensics has attracted more and more attention, and many forensic methods have been proposed for identifying image processing operations. Up to now, most existing methods are based on hand crafted features, and just one specific operation is considered in their methods. In many forensic scenarios, however, multiple classification for various image processing operations is more practical. Besides, it is difficult to obtain effective features by hand for some image processing operations. In this paper, therefore, we propose a new convolutional neural network (CNN) based method to adaptively learn discriminative features for identifying typical image processing operations. We carefully design the high pass filter bank to get the image residuals of the input image, the channel expansion layer to mix up the resulting residuals, the pooling layers, and the activation functions employed in our method. The extensive results show that the proposed method can outperform the currently best method based on hand crafted features and three related methods based on CNN for image steganalysis and/or forensics, achieving the state-of-the-art results. Furthermore, we provide more supplementary results to show the rationality and robustness of the proposed model.
Real Time Identification and Prevention of Doziness using Face Recognition System
(2017)

A large number of road accidents in the world occur due to driver’s not taking proper breaks while driving long distances. This System is used to detect fatigue behaviour of the driver well and before any accident occurs. In this system driver’s facial appearance is extracted via high speed camera which is placed in car in front of driver. This camera will capture the frames at every instant of time. Each of this frame will be processed using image processing algorithm. The mouth and eye region is separated from each of the frames and analyzed for concluding whether the driver is dozy or not. The system draws conclusion if the eyes are found closed for five consecutive frame. If doziness is detected, a signal alert is triggered. In case accidents occurs, message is sent to the desired number.
A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future
(2017)

A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.
Adaptive resource allocation for wildlife protection against illegal poachers
(2014)

Illegal poaching is an international problem that leads to the extinction of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, existing anti-poaching mechanisms are insufficient. This paper introduces the Protection Assistant for Wildlife Security (PAWS) application a joint deployment effort done with researchers at Uganda’s Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed applications with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important domain that promotes a wide range of new deployments. Additionally, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. In addressing these challenges, our first contribution is a behavioral model extension that captures the heterogeneity of poachers’ decision making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource allocation strategy against the learned model of poachers. Fourth, we demonstrate PAWS’s potential effectiveness when applied to patrols in QENP, where PAWS will be deployed.
Automatic knowledge extraction from OCR documents using hierarchical document analysis
(2018)

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format [1, 11, 18, 26]. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF.
What is principal component analysis?
(2008)

Several measurement techniques used in the life sciences gather data for many more variables per sample than the typical number of samples assayed. For instance, DNA microarrays and mass spectrometers can measure levels of thousands of mRNAs or proteins in hundreds of samples. Such high-dimensionality makes visualization of samples difficult and limits simple exploration of the data. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set1. It accomplishes this reduction by identifying directions, called principal components, along which the variation in the data is maximal. By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables. Samples can then be plotted, making it possible to visually assess similarities and differences between samples and determine whether samples can be grouped. Saal et al.2 used microarrays to measure the expression of 27,648 genes in 105 breast tumor samples. I will use this gene expression data set, which is available through the Gene Expression Omnibus database (accession no. GSE5325), to illustrate how PCA can be used to represent samples with a smaller number of variables, visualize samples and genes, and detect dominant patterns of gene expression. My aim with this example is to leave you with an idea of how PCA can be used to explore data sets in which thousands of variables have been measured.
DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter
(2015)

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figurative Language in Twitter. The proposed approach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indicate presence of figurative language in order to predict a fine-grained sentiment score. The method is supervised and makes use of structured knowledge resources, such as SentiWordNet sentiment lexicon for assigning sentiment score to words and WordNet for calculating word similarity. We have experimented with different classification algorithms (Naïve Bayes, Decision trees, and SVM), and the best results were achieved by an SVM classifier with linear kernel.
GPU-based cone beam computed tomography
(2010)

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.